code: def scale_tril(self) -> Tensor:
      Lxx = self.trunc.scale_tril
      Lyx = self._iLxx_Kxy.transpose(-2, -1)
      if "_orthogonalized_gauss" in self.__dict__:
          n = Lyx.shape[-2]
          Lyy = self._orthogonalized_gauss.scale_tril
          return block_matrix_concat(
              blocks=([pad(Lxx, (0, n))], [Lyx, Lyy])
          )
      return augment_cholesky(
          Laa=Lxx, Lba=Lyx, Kbb=self.gauss.covariance_matrix
      )
  
imports: from __future__ import annotations
  from inspect import getmembers
  from typing import Optional, Sequence, Union
  import torch
  from botorch.utils.probability.linalg import augment_cholesky, block_matrix_concat
  from botorch.utils.probability.mvnxpb import MVNXPB
  from botorch.utils.probability.truncated_multivariate_normal import TruncatedMultivariateNormal
  from linear_operator.operators import LinearOperator
  from linear_operator.utils.errors import NotPSDError
  from torch import Tensor
  from torch.distributions.multivariate_normal import Distribution, MultivariateNormal
  from torch.distributions.utils import lazy_property
  from torch.nn.functional import pad
label_desc: Use image node_id 8 for calling the UnifiedSkewNormal obj's underlying member method code with example usage: obj.scale_tril() and returns: augment_cholesky, block_matrix_concat